Chenxi Qiu

Dr. Chenxi Qiu is currently a tenure-track assistant professor in The Department of Computer Science and Engineering at University of North Texas (UNT). He directs the Mobile Computing Lab (MCLab) at UNT.

Before joining UNT, he worked as an assistant professor in the Department of Computer Science at Rowan University from 2018 to 2021, and worked as a Postdoctoral Scholar in The College of Information Sciences and Technology at Pennsylvania State University from 2016 to 2018.

Dr. Qiu received his Ph.D. degree in The Holcombe Department of Electrical and Computer Engineering at Clemson University in 2015 and his B.Engr. degree in Telecommunication Engineering from Xidian University in China in 2009.

Dr. Qiu’s research interests span the areas of mobile crowdsourcing and vehicle information systems. His research results have been published in highly-competitive conferences and journals (e.g., INFOCOM, AAMAS, SIGSPATIAL, IPSN, ICDCS, CIKM, Transactions on Networking (ToN), Transactions on Cloud Computing (TCC), Transactions on Mobile Computing (TMC), Transactions on Parallel and Distributed Systems (TPDS), and Transactions on Computers (TC)). His CV can be found here.

To prospective students: Multiple research assistant positions are available. I am looking for self-motivated students who are interested in the areas of mobile computing and spatial computing. If you are interested, please e-mail me with your CV and transcripts.

news

Jan 30, 2023	Our paper “User Customizable and Robust Geo-Indistinguishability for Location Privacy” was accepted by International Conference on Extending Database Technology (EDBT) 2023.
Jan 14, 2023	Our paper “Brake Signal-based Driver’s Location Tracking in Usage-based Auto Insurance Programs” was accepted by IEEE Internet of Things Journal.
Dec 11, 2022	Our demo paper “CORGI: An interactive framework for Customizable and Robust Location Obfuscation” was accepted by IEEE International Conference on Data Engineering (ICDE 2023)
Aug 22, 2022	Our paper “TrafficAdaptor: An Adaptive Obfuscation Strategy for Vehicle Location Privacy Against Vehicle Traffic Flow Aware Attacks” was accepted by ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL 2022).
Apr 4, 2022	Our paper “Distributed Consensus of data-sharing Decisions for Connected Autonomous Vehicles” was accepted by IEEE International Conference on Distributed Computing Systems (ICDCS 2022).
Jan 15, 2022	Mr. Sourabh Yadav joined our lab. Welcome Sourabh!
Oct 21, 2021	Our paper “CatCharger: Deploying In-motion Wireless Chargers in a Metropolitan Road Network via Categorization and Clustering of Vehicle Traffic” was accepted by IEEE Internet of Things Journal.
Jul 1, 2021	I started my new position as an assistant professor at University of North Texas.

selected publications

TMC 2020

[A+] Location Privacy Protection in Vehicle-Based Spatial Crowdsourcing via Geo-Indistinguishability

Chenxi Qiu, Anna Squicciarini, Ce Pang, and 2 more authors

IEEE Transactions on Mobile Computing [Impact factor = 6.075], 2020

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Nowadays, vehicles have been increasingly adopted in many spatial crowdsourcing (SC) applications. Similar to other SC applications, location privacy is of great concern to vehicle workers as they are required to disclose their own location to servers to facilitate the service utilities. Traditional location privacy protection mechanisms cannot be applied to vehicle-based SC since they assume workers mobility on a 2-dimensional plane without considering the network-constrained mobility features of vehicles. Accordingly, in this paper, we aim at addressing issues related to Vehicle-based spatial crowdsourcing Location Privacy (VLP) over road networks. Our objective is to design a location obfuscation strategy to minimize the quality loss due to obfuscation with geo-indistinguishability satisfied. Considering the computational complexity of VLP, by resorting to discretization, we first approximate VLP to a linear programming problem that can be solved by well-developed approaches. To further improve the time efficiency, we conduct constraint reduction for VLP by exploiting key features of geo-indistinguishability in road networks and problem decomposition based on VLPs constraint structure. Finally, we carry out both trace-driven simulation and real-world experiments, where our experimental results demonstrate the superiority of our approach over a known state-of-the-art location obfuscation strategy in terms of both QoS and privacy.
TMC 2018

[A+] An Energy-Efficient and Distributed Cooperation Mechanism for k -Coverage Hole Detection and Healing in WSNs

Chenxi Qiu, Haiying Shen, and Kang Chen

IEEE Transactions on Mobile Computing [Impact factor = 6.075], 2018

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Present approaches to achieve k-coverage for Wireless Sensor Networks still rely on centralized techniques. In this paper, we devise a distributed method for this problem, namely Distributed VOronoi based Cooperation scheme (DVOC), where nodes cooperate in hole detection and recovery. In previous Voronoi based schemes, each node only monitors its own critical points. Such methods are inefficient for k-coverage because the critical points are far away from their generating nodes in k-order Voronoi diagram, causing high cost for transmission and computing. As a solution, DVOC enables nodes to monitor others’ critical points around themselves by building local Voronoi diagrams (LVDs). Further, DVOC constrains the movement of every node to avoid generating new holes. If a node cannot reach its destination due to the constraint, its hole healing responsibility will fall to other cooperating nodes. The experimental results from the real world testbed demonstrate that DVOC outperforms the previous schemes.
INFOCOM 2016

[A+] Probabilistic demand allocation for cloud service brokerage

Chenxi Qiu, Haiying Shen, and Liuhua Chen

In Proceedings of IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications, Apr 2016

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Functioning as an intermediary between cloud tenants and providers, cloud service brokerages (CSBs) bring about great benefits to the cloud market. To maximize its own profit, a CSB is faced with a challenge: how to reserve servers and distribute tenant demands to the reserved servers such that the total reservation cost is minimized while the reserved servers can satisfy the tenant service level agreement (SLA)? Demand prediction and demand allocation are two steps to solve this problem. However, previous demand prediction methods cannot accurately predict tenant demands since they cannot accurately estimate prediction errors and also assume the existence of seasonal periods of demands. Previous demand allocation methods only aim to minimize the number of reserved servers rather than the server reservation cost, which is more challenging. To solve this challenge, we propose a Probabilistic Demand Allocation system (PDA). It predicts demands and more accurate prediction errors without the assumption of the existence of seasonal periods. It then formulates a nonlinear programming problem and has a decentralized method to find the problem solution. In addition to overcoming the shortcomings in previous methods, PDA is novel in that rather than separately conducting the prediction and demand allocation, it considers prediction errors in demand allocation in order to allocate demands with offsetting prediction errors (e.g., -1 and +1) to the same server, which helps find the problem solution. Both simulation and real-world experimental results demonstrate the superior performance of our system in reducing servers’ reservation cost.
ToN 2015

[A+] CEDAR: A Low-Latency and Distributed Strategy for Packet Recovery in Wireless Networks

Chenxi Qiu, Haiying Shen, Sohraab Soltani, and 3 more authors

IEEE/ACM Transactions on Networking, Oct 2015

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Underlying link-layer protocols of well-established wireless networks that use the conventional “store-and-forward” design paradigm cannot provide highly sustainable reliability and stability in wireless communication, which introduce significant barriers and setbacks in scalability and deployments of wireless networks. In this paper, we propose a Code Embedded Distributed Adaptive and Reliable (CEDAR) link-layer framework that targets low latency and balancing en/decoding load among nodes. CEDAR is the first comprehensive theoretical framework for analyzing and designing distributed and adaptive error recovery for wireless networks. It employs a theoretically sound framework for embedding channel codes in each packet and performs the error correcting process in selected intermediate nodes in a packet’s route. To identify the intermediate nodes for the decoding, we mathematically calculate the average packet delay and formalize the problem as a nonlinear integer programming problem. By minimizing the delays, we derive three propositions that: 1) can identify the intermediate nodes that minimize the propagation and transmission delay of a packet; and 2) and 3) can identify the intermediate nodes that simultaneously minimize the queuing delay and maximize the fairness of en/decoding load of all the nodes. Guided by the propositions, we then propose a scalable and distributed scheme in CEDAR to choose the intermediate en/decoding nodes in a route to achieve its objective. The results from real-world testbed “NESTbed” and simulation with MATLAB prove that CEDAR is superior to schemes using hop-by-hop decoding and destination decoding not only in packet delay and throughput but also in energy-consumption and load distribution balance.
INFOCOM 2014

[A+] Energy-efficient cooperative broadcast in fading wireless networks

Chenxi Qiu, Haiying Shen, and Lei Yu

In IEEE INFOCOM 2014 - IEEE Conference on Computer Communications, Apr 2014

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Cooperative broadcast, in which receivers are allowed to combine received packet from different senders to combat transmission errors, has gained increasing attention. Previous studies showed that broadcast optimization solutions are sufficient in non-fading environments but may suffer a low delivery ratio under wireless channel fading. Though previous work analyzed the tradeoff between energy and delay in cooperative broadcast, no works investigated the tradeoff in a fading environment. Thus, in this paper, we study this tradeoff with the consideration of fading. We formulate this problem as a Fading-resistant Delay-constrained Minimum Energy Cooperative Broadcast (FDMECB) problem, and prove that it is NP-complete. We then propose an approximation algorithm for theoretical interests. We further propose a heuristic algorithm that makes approximately optimal local decision to achieve global optimization. Our experimental results show that our algorithms outperform a previous non-fading resistant algorithm.